Bibliographic record
Abstract
In recent years, scholars have noted a broadening of environmental justice research, both geographically and conceptually, to include global issues that expand beyond the spatial distributions of environmental ‘goods' and ‘bads' to include other dimensions including recognition, participation, and capabilities. The purpose of this report is to consider how this broadening has influenced research topics and themes, the geographic locations of researchers, and the future course of environmental justice research. We searched for articles identifying environmental justice as a keyword from 2000 to 2009 in Scopus, Web of Science, and GeoBase. While the number of articles published looking at environmental justice has increased over the past decade, our search contradicts the previous claims that environmental justice research has greatly expanded conceptually or geographically beyond its traditional origins. Our findings show that environmental justice research remains firmly rooted in the United States, focusing on the distributional aspects of environmental harms. We introduce alternative research methodologies that can be used to inform a more holistic and just approach to research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".